Jul 2026· Data mining and knowledge discovery· Vol 40· 0 citations· 95 references
Computer Science
TL;DR
From this literature review, it becomes evident, in face of a series of preliminary and promising studies, the field of XRL still lacks a proper accounting towards full explainability and it emerges that more effort should be devolved into developing paradigms with a human-in-the-loop factor and standardized metrics should be adopted to allow a fair comparison of different explanation methods.
This work employs Inductive Logic Programming (ILP) to extract symbolic representations of RL policies and define a novel set of explainability metrics, including activation rate, feature coverage, syntactic distance and semantic distance, which provide crucial insights for the transfer and generalization of action-specific policies.
Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand. This can lead to diminished user trust, and makes for a more challenging verification of systems. To address this challenge, this paper introduces Explanations using Alternative Realities for Reinforcement Learning (EARL), a Python library to produce counterfactual explanations in RL settings. This library allows the user to produce explanations by exploring What-if scenarios to clarify agent behavior by comparing possible outcomes. Counterfactual explanations have been shown to be intuitive and user-friendly in psychology research, but have only recently been explored in RL, with existing implementations usually limited to toy examples and benchmarks. EARL supports counterfactual explanation generation in realistic RL-based self-adaptive systems. To demonstrate its applicability, we demonstrate its use in a simulation of CitiBikes, a self-adaptive bike-sharing system, and we provide evaluations showing how it performs in real applications.
Jasmina Gajcin, Juan C. Rosero, Ivana Dusparic· 0 citations
This work reviews pre-2019 XRL approaches, categorizing them into policy explanation, reward decomposition, model transparency, and post-hoc interpretability methods, and proposes a framework that combines interpretable policies, surrogate models, attention mechanisms, and visualization techniques to enhance transparency without significantly reducing performance.
Michael Anderson, David Thompson· International Journal of Art...· 0 citations
In the quantitative finance area, particularly in order execution, reinforcement learning (RL) has shown great promise due to its ability to interact with market environments based on real data. However, traditional RL methods suffer from slow research speed and rely on static market assumptions, which do not consider the impact of the agent's execution action on the environment. To address these, we propose a Self-Evolutional single-agent/multi-agent Reinforcement Learning (SE-RL) framework. The framework utilizes a Large Language Model (LLM) to design various RL algorithm modules, such as agent model design, reward function, profiling, communication, and state imagination, by leveraging the LLM generating module output or code. SE-RL could continuously improve the accuracy of LLM-generated RL algorithms through a dual-enhancement kit at both high-level (prompt refinement) and low-level (parameter fine-tuning). Additionally, we use a multi-agent system to simulate dynamic financial markets, accounting for the impact of order executions on market dynamics. To further enhance training in such a dynamic market, we develop a hybrid environment training method that could rebalance each environment's loss weight. Comprehensive experiments on 200 realistic stock datasets demonstrate that our proposed framework outperforms current state-of-the-art baselines. Project page: https://kdd2026-se-rl.github.io/.
Vincent Fu, Xin-Xin Xu, Weichen Xu et al.· Proceedings of the 32nd ACM...· 0 citations
This work deconstructs the RL post-training algorithm, investigating each step to clarify what is actually happening beneath the surface, and uses the entropy of the policy's output distribution as a lens to compare the distributions learned through pretraining, SFT, and RL post-training, revealing how each stage shapes model certainty.
D. Clay, Saket Gollapudi, Sankar V Harilal et al.· 0 citations
The aim of this paper is to describe the potential autonomous advancements the next generations of large language models may evolve and want to offer some suggestions as a theoretical and a technical framework.
Mengbo Song· Mathematical Modeling and Al...· 0 citations
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